TQCPat: Tree Quantum Circuit Pattern-based Feature Engineering Model for Automated Arrhythmia Detection using PPG Signals
Our results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.
Authors
- Ru‐San Tan (ORCID: https://orcid.org/0000-0003-2086-6517)
- Mehmet Ali Gelen (ORCID: https://orcid.org/0000-0002-6018-7976)
- Şengül Doğan (ORCID: https://orcid.org/0000-0001-9677-5684)
- Mehmet Bayğın (ORCID: https://orcid.org/0000-0001-6449-8950)
- Türker Tuncer (ORCID: https://orcid.org/0000-0002-5126-6445)
- U. Rajendra Acharya (ORCID: https://orcid.org/0000-0003-2689-8552)
- Prabal Datta Barua
Institutions
- Fırat University (TR)
- University of Southern Queensland (AU)
- Erzurum Technical University (TR)
- Duke-NUS Medical School (SG)
- Elazığ Eğitim ve Araştırma Hastanesi (TR)
- National Heart Centre Singapore (SG)
Publication Details
- Journal
- Journal of Medical Systems
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1007/s10916-025-02169-0
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 4.29